KR20040097210A - System and method for predictive ophthalmic correction - Google Patents

Abstract

The present invention is a system and method for providing predictive results in the form of optimal predictive indications for therapeutic ophthalmic correction of vision defects in a patient. The optimal predictive indication is derived from new information analyzed in combination with future treatment outcome impacts, optimized historical treatment outcome information. This indication is preferably an optimized, customizable photodissection algorithm for driving the photodissection excimer laser. This indication provides the rate.

Description

Predictive eye correction system and method {SYSTEM AND METHOD FOR PREDICTIVE OPHTHALMIC CORRECTION}

A significant percentage of the population has vision defects, commonly referred to as near-sightedness and far-sightedness, and often accompanied by defects known as astigmatism. Myopia and hyperopia are the result of low-order optical aberration called defocus. Simple astigmatism is also low-level aberration. In brief, a perfectly myopic eye causes all incident parallel light to focus in front of the retina, and a perfectly primitive eye gathers all incident parallel light into a focal posterior to the retina, and simply the eyes of astigmatism A portion and a portion of the light in the vertical line are focused at a predetermined separation distance from the retina. For a long time, doctors have attempted to accurately measure these defects and correct them with glasses, contact lenses and other devices and / or procedures. Popular treatment procedures use corneal geometry to refocus incident light using a laser beam (excimer laser, typically having a wavelength of 193 nm), suitable for photoablate volumetric portions of the exposed conical surface. The development continued by transforming

Refractive keratectomy (PRK), laser keratoplasty (LASIK), and laser epithelial keratoplasty (LASEK) are examples of photorefractive refractive surgery to correct the optical defects described above.

It is also possible to make accurate measurements, known as high-level optical aberrations, for example with advanced diagnostic techniques such as wavefront sensors. These high-level aberrations cause poor vision quality by reducing the sensitivity and / or contrast sensitivity that results from defects in the overall optical system of the eye and causes glare, night blindness and other ways. Not surprisingly, device manufacturers and physicians can eliminate, minimize or balance these aberrations to correct or practically correct vision for technology, means and devices and treatment limitations of 20/8 (known as supervision). Or in response to a treatment procedure intended to optimize vision quality by other methods intended for high stage defects.

For a variety of known causes and not yet found, the intended outcomes of optimized photorefractive surgery and custom lens applications include, for example, undefined contact, inlay and IOL. ” In order to better understand the kinetics of correction, the focus is on eye structure, physiology, and sophisticated modeling, and interested readers are on pages 407-413 of Jour.Ref.Surg. 16 (July / August 2000). See the article by Cynthia Roberts, Ph.D., "The Corneal Is Not a Plastic Fragment." Dr. Robert says that if the cornea resembles a homogeneous piece of plastic, the biomechanics of the structural alteration incision is Since no reaction occurs, it is assumed that a procedure known as radial keratotomy (RK) cannot be performed (RK similarly to the spoke of the wheel, flattening the cornea with a series of incisions). Surgical procedures designed to correct myopia.) The biomechanics of the eye, particularly the cornea (the biomechanical response of the eye to aggressive stimuli), have a significant effect on the results of laser vision correction. Robert's book reported changes in the shape of the anterior cornea due to corneal resection (flap cut) prior to laser ablation, such as laser cutting of the corneal lamina prior to LASIK and PRK procedures. By understanding that the biomechanical corneal response to aggressive stimuli is not a piece of plastic but rather a series of stacked rubber bands (lamellas) with sponges between each layer (interlayer space filled with extracellular cytoplasm). It can be explained by Robert: The rubber band has an internal pressure that pushes it out from the bottom and the end is cut by a limbus. It is assumed to have tension because it is held indefinitely: the water content of each sponge is dependent on whether each rubber band is stretched in. A large tension squeezes more water out of the sponge, reducing the inner bed space, eg For example, the cornea becomes flat. Thus, the action of laser surgery itself to reshape the cornea can alter the biological structure of the cornea, which is not seen as it appears. US Patent Application Publication No. 2002/0103479, issued to Sarver, discusses the optimization of predictiveness of vision correction methods using surgical results of repeated analysis to produce optimal treatment results. PCT Application Publication No. WO 00/45759 discusses the interaction between the photoresection laser system used and the wound healing response of the eye and provides calibration parameters for the calculation of the eye's healing response in the range of ± 1000x Zernike. It was concluded that the Zernike coefficients should be inserted in the sum of the Zernike polynomials. US Patent Publication No. 2002/0007176 discusses radial dependent ablation efficiency in the form of modified polynomials based on optical path differences between wavefront and plane waves measured from the patient's eye. In many instances, surgeons will modify the manufacturer's treatment profile by their professional nomogram, which typically provides only power shift correction. However, personal variations of this format are generally based on relatively small samples of patients and procedures, so general utility and optimization cannot be achieved. US Patent No. 5,891,131 entitled "Automated Simulation and Corneal Refractive Procedure Design Method and Apparatus" describes a computerized finite element method for simulating a specific corneal deformation of a patient following corneal incision and / or corneal ablation procedure. do. The patient provides a general infrastructure for this type of approach but does not appear to solve the problem of optimized predictive analysis. A comprehensive review of the finite element method to simulate human refractive corneal surgery concludes that subsequent work needs to refine the analysis and include other effects and phenomena that may be important in corneal modeling. Described in a 1994 paper. All these efforts are focused on modifying ablation algorithms or nomograms for more accurate predictions and on demand to achieve the desired and desired refractive results by manufacturers and practitioners. However, no matter what the name is, a piece of puzzle that shows perfect vision, supervision, normal vision or optimal vision quality is still missing. For example, induced spherical aberrations and other high-level aberrations are known conventional post-LASIK effects that lead to sequelae of visual defects and suboptimal visual quality. However, the cause and elimination of such treatments that cause aberrations continue to challenge manufacturers and physicians.

In view of the foregoing advances, the inventors have found that the hardware facilitates therapeutic eye procedures, particularly photorefractive refractive vision correction and, optionally, customized ophthalmic vision, which results in optimal vision quality and greater patient satisfaction. Recognized the need for software and methods.

This application prioritizes US Patent Provisional Application No. 60 / 368,643, filed March 28, 2002, which is hereby incorporated by reference in its entirety, and US Patent Provisional Application No. 60 / 340,292, filed December 14, 2001. Insist.

The present invention relates generally to technical and business methods for the correction of ophthalmic defects. In particular, the present invention relates to systems, instructions, and methods for providing predictive results for therapeutic ophthalmic correction of vision disorders. The present invention is directed to providing a higher degree of vision quality of patients due to vision correction procedures.

1 is a block diagram of a system in accordance with a preferred embodiment of the present invention.

2 is a block diagram of a system according to another preferred embodiment of the present invention.

3 is a block diagram of an example LASIK system in accordance with the present invention.

4 is a block diagram / flow diagram illustrating a method according to an embodiment of the present invention.

FIG. 5 is a chart showing the distribution of preoperative high-stage (tertiary, fourth and fifth Zernike stages) aberrations for a clinical study group of 92 eyes.

FIG. 6 is a graph showing the RMS magnitude of LASIK induced high-step aberrations over time.

7 is a graph showing the RMS size of the LASIK induced high-stage aberration without spherical aberration with time.

8 is a graph based on linear regression analysis showing predicted vs measured values of spherical aberration after LASIK in accordance with an embodiment of the present invention.

9 is a graph based on linear regression analysis showing predicted vs measured values of spherical aberration after LASIK in accordance with an embodiment of the present invention.

10 is a graph showing hardware related to an embodiment of the present invention.

11 is a schematic of simple neural association model related data based on training.

12 is a diagram illustrating the performance of a web foundation model for analyzing results and measuring ablation patterns.

Figure 13 is a block diagram of a structure for a business model according to an embodiment of the present invention.

Figure 14 is a schematic diagram showing the layered fibrous layer of the cornea.

Figure 15 is a schematic diagram showing the term definitions used in the description of the present invention.

Figure 16 is a schematic chart illustrating the application of pressure to the eye.

17 is a block diagram of a configuration for a finite element model of an eye according to an embodiment of the present invention.

18 is a computer simulation of a finite element mesh in accordance with an embodiment of the present invention.

19 is a computer simulation of a stratified solid element of a finite element model in accordance with an embodiment of the present invention.

20 is a perspective view showing two dimensions of the layered elements of the finite element model according to an embodiment of the present invention.

FIG. 21 is a computer simulation of a layered solid element similar to FIG. 19 showing the separated segments.

Figure 22 is a flow chart according to the method of an embodiment of the present invention.

Figure 23 is a cutaway view of a computer simulation of a pressurized cornea according to an embodiment of the present invention.

24 is an enlarged view of the pressing area of FIG.

The present invention is directed to an apparatus and method for enabling the prediction of the results of a proposed therapeutic ophthalmic correction that includes a photorefractive refractive surgery and custom eye vision and supports a treatment model for providing predictive results. Numerous clinical reviews have been studied in which a combination of non-single or simple coefficients is indicated and calculated to appear or to be drawn to predict the difference between the computed or desired photorefractive refractive results and the actual results. In other words, the surgical procedures / techniques or resection algorithms used to treat today's myopia patients do not guarantee to produce the same results, even when used in similar future myopia patients. However, it has been interesting to note that consistency and standardization in all aspects of photorefractive refractive surgery yield better treatment (calibration) results. Accordingly, embodiments of the present invention provide the best predictive instruction for physician use (eg, visual zone size, corneal incision depth, ablation algorithm for driving a therapeutic laser) to optimize the results of the proposed vision defect correction. Results of the optimized treatment and history, etc., resulting in the use of data of the optimized optimized treatment and history. To illustrate this, it is assumed that over 1000 progression of myopia correction procedures, the surgeon enters all parameters through the impact of these procedure results into a computer statistical analysis program. These parameters may include, for example, patient profile information (e.g. refractive power, history, culture, etc.), physician skills (nomograms, historical result data, etc.), facility specifications (e.g., laser manufacturing, model and operation). Parameters, software versions, principles of diagnostic testing, etc., diagnostic procedures (e.g., excisionology, elevation based on local anatomy, ultrasound, OTC, etc.), environmental conditions (e.g., temperature, humidity, time, etc.) And other variables that are not described, but are not limited to these. The computer program can be analyzed to determine these historical data, for example statistically significant parameters and their relationship to past therapeutic outcome success. For today's patient # 1001 with a known myopia defect, the surgeon enters the computer with the new and expected appropriate parameters by manual or automatic means. In turn, the computer can be accessed and analyze this information in terms of optimized treatment and history information, for example an optimized laser for driving a therapeutic laser system, which is the prediction of an optimized result for correction of measured defects. Generate a result prediction indication, such as an incision shot profile algorithm.

In accordance with this exemplary description of the invention, embodiments of the invention provide a plurality of predictive therapeutic outcome impact information (photogrammatically related to at least a patient and / or physician and / or diagnostic measurement and / or treatment state and / or environmental condition). A system that provides predictive results for a proposed therapeutic ophthalmic correction that includes a collection and transmission station (or platform) for accommodating the non-limiting data of the ablation surgery and for transmitting a plurality of information to the computational section. Explain about. The computational section accommodates a plurality of pieces of information and influences predictive treatment outcomes of treatment and history related to at least patient and / or physician and / or diagnostic measurements and / or treatment conditions, treatment treatment plans, actual outcome data and / or environmental conditions. A plurality of histories of therapeutic outcome information derived from an optimal analysis of the information can be stored and provide an analyzed output that is an optimal predictive indication for obtaining improved therapeutic ophthalmic correction. In one aspect of this embodiment, the collection and transmission station is any of a variety of analytical devices (e.g. wavefront sensors, local anatomy, corneal thickness meters, tonometers, etc.), treatment systems (e.g. excimer lasers, custom eye lenses). Platforms, etc.), computer stations interfaced by hardware and / or software means for the operating room “weather station” and / or provide means for the physician to enter other predictive appropriate new data. In these and other embodiments in accordance with the present invention, some or all of the novel result impact information may be automatically collected by various devices and transmitted to a computing device, or may be performed via a keypad or other known means. Entered manually by the assistant or patient.

In various aspects of the invention, the computing station may be part of a local, internal office system, or, optionally, may be a network and / or internet based remote server. Transmission to and from the computing station is facilitated by any waveguide based or wireless means, or by a portable medium such as a CD or disk. Advanced routing media can ensure internet transmission.

Software and data structures for performing optimal analysis of therapeutic and actual historical treatment results and analysis of new information to generate and provide optimal predictive indications can be performed with a variety of approaches. Preferably, however, non-limiting examples include statistical analysis (eg, multiple linear regression), multidimensional vector (matrix) analysis, neural networking and finite element analysis (FEA). The databases consist of, for example, individual physician data, FDA clinical data, up-to-date updated third party data, manufacturer clinical data, and the like. Computer stations, network servers, analysis devices, treatment devices, and interface hardware and software are all within the invention and are themselves part of the invention as part of the invention.

Optionally, embodiments of the present invention are directed to viable instructions contained in means executable on an end user control device that can be used to provide predictive results for therapeutic ophthalmic correction.

In another embodiment, the present invention provides an optimized history resulting in diagnostic and / or therapeutic components, a graphical user interface (GUI) with a display, and a result prediction indication for a proposed vision correction procedure provided by a data structure. An ophthalmic diagnostic and / or therapeutic system comprising a selection device that facilitates the selection of collected information for analysis into information.

Another embodiment according to the present invention describes a method for providing predictive results for proposed treatment ophthalmic correction. The method includes collecting “new” information of a plurality of treatment outcome impacts including at least ophthalmic defect information for the patient, and a data structure comprising treatment outcome information of the therapeutic and actual history optimized for the determined eye defects. Providing the new information to the computing platform to accommodate the optimal prediction for the proposed corrective treatment of the defects of the eye determined on the basis of the analysis of the new treatment result impact information in combination with the historical result information via the computing platform. Generating an instruction. A preferred aspect of this embodiment describes a method of providing rate and treatment based prediction results as a business model.

In all the foregoing embodiments, the preferred optimization approach includes any of the FEAs combined with the parameters of statistical analysis, matrix analysis, neural networking or corneal ultra structural model (CUSM). Preferred diagnostic stations include aberometers such as, for example, the branded Zywave ^™ wavefront analyzer and the Orbscan corneal analyzer (Bache & Rom, Inc., Rochester, NY). The treatment station is for example trademarked Technolas 217Z utilizing the trademark Planoscan or the trademark Zylink ^™ software platform (Bache & Rom, Inc., Rochester, NY). 217 ^TM ) excimer laser, such as an excimer laser system, comprising an excimer laser that is a flying spot, the preferred treatment procedure is LASIK, and the preferred optimal predictive instruction is a custom ablation algorithm for driving the modified laser, but the present invention is described above. As it is not limited thereto.

These and other objects of the present invention will become apparent from the following detailed description. However, various changes and modifications within the spirit and scope of the present invention will become apparent to those skilled in the art based on the detailed description and the appended drawings and claims, and thus the detailed description and specific examples indicating preferred embodiments of the present invention. Is given only for the city.

1 illustrates a system 100 that provides prediction result indication for a proposed therapeutic ophthalmic correction. The result is achieved by custom LASIK treatment or, for example, customized retreatment for dissection ablation, to correct low and high stage deviations that cause visual defects in the patient's eye 120. However, the capture, feedback, and analysis of the data does not limit the present invention to LASIK, and the planning and implementation of the present invention provides examples, as well as the design and performance of custom ophthalmic vision, including contact lenses, IOL's, inlays and onlays. For example, it can be applied to PRK and LASEK. The collection and transmission station 102 is shown in the form of a wavefront sensor. The wavefront sensor 102 preferably measures optical aberrations prior to surgery in the eye's eye 120 of the patient, up to the fifth order, in certain cases the seventh Zermiq step or equivalent. Exemplary wavefront sensors, which are not part of the present invention and are themselves part thereof, are disclosed in US Pat. No. 5,777,719 to Williams et al., The content of which is incorporated herein in its entirety to the extent permitted by applicable patent laws and regulations. Incorporated by reference in. Obvious refraction of the patient's eye can be obtained from wavefront sensor data, for example, as disclosed in co-owned pending US Application No. 60 / 284,644 filed April 28, 2001. Obvious refractive data and high step aberration data represent a subset of the predicted treatment outcome impact information 105 for the patient. Arrow 104 represents the predicted treatment outcome impact data for, for example, a physician, other diagnostic measurement, treatment status, and / or environmental condition. Schematically, doctors manufacture wavefront sensors and lasers (therapeutic devices) used to correct vision defects in patients, any other information that has a predicted effect on the outcome of an actuated standby or on-demand photoresection procedure, In addition to model and principle of operation information, it may be desirable to enter historical results data and personal nomogram information for visual acuity defects similar to those currently measured. As another example, the physician may wish to optimize subsequent surgical spherical aberrations (or others) to improve low light visual quality, and thus include spherical aberrations that are predicted as specific input parameters.

All this information 105, 104 is entered or collected into the collection and transmission platform 102 either manually or automatically and transmitted to the computing station 110 as shown by reference numeral 103 as "new" information. The transmission 103 may be performed by known means, including but not limited to, direct, internet, telephone data transmission, wireless communication, CD, disk, and the like. As such, computing station 110 may be located locally or remotely, for example, in a physician's room. In any case, the computing station is capable of accepting new or historical inputs from other sources indicated by arrow 106 and described in more detail below.

Computation section 110 preferably operates with three functional capacities. One of these doses is to accommodate the "new" predicted treatment outcome impact information 105 as described above. In the second capacity, the computing station includes a storage medium, for example disk space, and an appropriate data structure (described below) that can receive and / or generate treatment result information 112 of optimized theoretical real history. do. This historical information is derived from the optimal analysis of actual historical data, predicted treatment outcome impact information, and treatment plan associated with the patient, physician, clinical, treatment, environmental condition, and the like. For example, a physician may have 1000 previous LASIK procedures performed. Each procedure for correcting a patient's measured visual impairment resulting from a particular clinical measurement may be performed using a specific clinical device, a specific laser system with an ablation profile driving algorithm changeable by the surgeon's personal nomogram, and a specific corneal incision for flap generation. Obtained with the aid of LASIK. The standby operating room state provides the environment in which each procedure is performed. And each therapeutic procedure is characterized by the results described above and the effects (postoperative results measured during the follow-up period) that are known and predicted by some or all of the others. By performing analysis of the new input data in association with the optimized historical data and previous optimized instructions for the proposed therapeutic procedure (therapeutic surgical plan), the resulting predictive therapeutic relationship can be determined. When “new” information relating to the 1001th procedure is provided for analysis associated with the stored historical result information 112, 114 ′, the computation section 110 is optimized to facilitate optimization correction of ophthalmic defects in the patient. It may be operated with a third functional capacity for outputting the predictive indication 116 to the pseudo (or laser system) 118 (shown at 114). This optimal prediction indication is preferably a custom algorithm used to drive the phototomy facility and the procedure, but may include other optimization information appropriate for the procedure, such as, for example, LASIK flap thickness and / or optical zone size. have.

The approach for generating an optimal prediction indication 116 in accordance with the present invention includes various preferred embodiments. The first embodiment utilizes multiple linear regression to provide, for example, a statistical analysis of actual and treatment history result data 112, 114 ′ that can be used in conjunction with new input data 104, 105. . The basis of this embodiment is shown as described below with reference to FIGS. FIG. 5 shows the distribution of what is referred to herein as high-level aberration (third, fourth and fifth Zernik stages) among 92 preoperative eyes from a group of clinical study samples. As shown, the cubic aberration (Z _3xy ) represents most of the preoperative wavefront aberration of the standard population, and the (negative) spherical aberration (Z ₄₀₀ ) is also quite large. One known effect of conventional LASIK treatment is the induction of spherical aberration, which can cause reduced vision under high stage aberrations, especially under low light conditions. 6 shows 46 eyes treated with LASIK with the registered trademark Planoscan (Roche, Rochester, NY, USA) and Bache & Rom Inc., Rochester, NY, ) High-level aberration measured (RMS) measured at 1 month intervals for preoperative and 3 months postoperatively of 46 eyes treated with LASIK with Zyoptix®. PlanoScan relates to a conventional (out-of-focus, astigmatism) LASIK treatment algorithm and to a custom LASIK treatment algorithm associated with a software platform that is a registered trademark gylink (Bacheth & Rom Incorporated, Rochester, NY). Zyoptix is designed to correct measured preoperative wavefront aberration. FIG. 7 is a graph similar to FIG. 6 except that the spherical aberration condition Z ₄₀₀ has been removed to show only the distribution of other third, fourth and fifth Zernik conditions.

Stepwise multiple linear regression was used to treat the eye with Zyoptics and Planoscan, especially with three different pupil sizes of 5.0 mm, 6.0 mm and 7.0 mm to investigate the predictive nature of the relationship between postoperative spherical aberration and preoperative measurements. All preoperative 3rd and 3rd Zernik coefficients are used to predict 3 months of spherical aberration (Z ₄₀₀ ). The relationship between the eye treated with Zyoptix and the 5.0 mm pupil (n = 51),

3MonthZ ₄₀₀ = PreOpZ ₄₀₀ * 0.387686 + PreOpZ ₂₀₀ * 0.034882 + 0.023291

Gives the interdependence coefficient r = 0.75. The relationship between the eyes treated with Zyoptics and a 6.0 mm pupil (n = 46),

3MonthZ ₄₀₀ = PreOpZ ₄₀₀ * 0.501336 + PreOpZ ₂₀₀ * 0.052621 + 0.042704

Gives the interdependence coefficient r = 0.80. Relationship of 7.0 mm pupils (n = 23) in eyes treated with Zyoptix,

3MonthZ ₄₀₀ = PreOpZ ₄₀₀ * 0.356462 + PreOpZ ₂₀₀ * 0.070921 + 0.068812

Gives the interdependence coefficient r = 0.72. In Fig. 8, shown for 6.0 mm pupils, there is a significant agreement between the observed value and the value predicted using this equation. A pupil of 5.0 mm in the eye treated with a planoscan, relationship at n = 52,

3MonthZ ₄₀₀ = PreOpZ ₄₀₀ * 0.933579 + PreOpZ ₂₀₀ * 0.023760 + 0.004549

Gives the interdependence coefficient r = 0.84. A pupil of 6.0 mm in the eye treated with the Planoscan, relationship at n = 46,

3MonthZ ₄₀₀ = PreOpZ ₄₀₀ * 0.745150 + PreOpZ ₂₀₀ * 0.037653 + 0.020633

Gives the interdependence coefficient r = 0.84. A pupil of 7.0 mm in the eye treated with a planoscan, relationship at n = 23,

3MonthZ ₄₀₀ = PreOpZ ₄₀₀ * 0.638732 + PreOpZ ₂₀₀ * 0.055682 + 0.069077

Gives the interdependence coefficient r = 0.81. In Fig. 9, which shows 6.0 mm pupil data using this equation, there is a significant agreement between the observed and predicted values. Thus, "new" information (preoperative spherical aberration) is statistically analyzed "history" information (pupillary size, postoperative spherical aberration, out-of-focus) to generate predictive instructions for optimizing the patient's 3-month postoperative spherical aberration. Is analyzed in association with.

According to another embodiment, multiple variable matrix approaches may be used to provide optimal prediction indications. Current procedures for determining an ablation profile based on thin lens formulas are limited by various drawbacks. For example, biomechanics and healing reactions are not considered, and a simple use of the Munnerlyn formula leads to a tissue removal profile based only on refractive index changes. In addition, current linear approaches cannot adjust for differences in individual procedures during surgery. The result obtained from both of these is refractive index control through a personalized nomogram without any practical means to achieve aberration correction control.

Illustratively, let Z be the vector representing the Zernik vector output from the aberometer for the corneal surface to be removed.

The wavefront data output from the aberration system is modified by the refractive index n of the cornea. M 'with conditions describing the interdependence of various Zernike conditions affecting wavefront and non-wavefront information such as, for example, topical or other preoperative patient data, is defined as the clinical matrix. For example, M 'may be a diagonal matrix.

Matrix element C _ij is a condition obtained from multiple linear regression of the aforementioned preoperative and postoperative spherical aberration measurements. As the interdependencies between the various Zernike conditions are typically more spherical through clinical studies, M 'is filled as a complete nxm matrix. Another matrix M "can be generated from actual and predictive history result information. In that form,

Preferably, M ″ may be deployed with the same analysis software as used at M ′ to develop a feedback loop to regularly update M ″ to be reflected in the surgical procedure. The resulting matrix Z '= M "x M' x (constant) represents information for generating optimized predictive information for correcting visual acuity defects in a patient. In a broad aspect of this embodiment, M" is from a plurality of sources. The information can be included and act as a central database for providing predictive instructions to any physician wishing to utilize a service providing such information. In this case, M ″ may be updated with new information available. The updated information may be obtained from multiple sources through various acquisition plans, including the purchase or rental of appropriate information.

In a different embodiment of the invention described with reference to FIG. 11, the neural networking environment 2000 is an approach that can provide the physician with optimal prediction instructions. Neural networking, often referred to as neuro-computing, is a fundamentally novel approach to information processing, and is the first viable alternative to sequentially programmed operations. Neural networks offer distinct advantages for applications with little or no knowledge of how to deploy algorithms. The neural network can operate with inaccurate or ambiguous data and can be manipulated to generate reliable predictions from historical information. The neural network can be applied to external input by modifying the stored data according to a particular learning method. They can in turn determine the shape of the network (connections) by mapping them. Although there are often many solutions to any problem, the advantages of neural networks are obtained from network learning to produce optimal solutions or results. In accordance with an embodiment of the present invention, improving refractive surgery results can be seen as a prediction of a resection algorithm in which a large number of different sets of patients, diagnostic and historical data and desired results are given. As many of the data provided to determine the ablation algorithm prove to be difficult to analyze and determine interaction coefficients by traditional statistical methods, neural computations are ideal for analyzing a broad basis of diagnostic data and providing optimized prediction results. Will prove as a tool. The neural network may function in a back-propagation mode as described in FIG. 11 showing a simple neural computational model 2000. All appropriate preoperative data (preoperative outcome impact information) applicable to the outcome of the procedure are input to the buffer layer 2001. The hidden layer 2003 may consist of historical information belonging to a third party and for which the system is to test and learn from the data and results present. By understanding the historical results from past procedures, the hidden (analysis) layer 2003 is trained to perform appropriate computations to achieve the desired results by pre-allocating known additional factors for generating intermediate results. As new patient data, treatment outcome data and actual outcome data are available, the hidden layer 2003 continues training to output optimal prediction indications in the output buffer 2005.

A unique feature of neural networks is that they can be trained from existing sets of data and known solutions to update hidden layers with additional functions and rules to improve the results from future information. As the known result database grows, the network that produces the optimal solution becomes more efficient. The neural computational model may preferably be implemented with web-based application models 3000 and 4000 as shown in FIGS. 12 and 13, respectively. All of the information 3002, 3004 may be collected at the computation point 3006 where data analysis is complete and the optimal prediction indication output 3008 returns to the client 3010. The input and output may preferably be through a web based application whose interface has a computational structure 4000 shown in FIG. Rule box 4001 refers to the computer software and analysis techniques required to complete the process. The system is limited so that it can be easily extended to support any size client base. This represents the scalable structure of the standard for web-based business.

The fourth approach implemented in this embodiment is an input using an accurate corneal superstructure model (CUSM) to obtain Young's modulus and Poisson's ratio information for the eye, and the novel as described above. Relevant to finite element analysis (FEA) using calibration finite elements associated with one input data. It has been proposed that a suitable biomechanical model of the eye includes both the structural model of the cornea provided by the superstructural fiber model and the hydrodynamic analysis based on the hydrate matrix elements. These two aspects of the corneal system, referred to herein as the corneal superstructural model (CUSM), outline the outline as follows.

When tested on the macroscale, biological tissues are anisotropic and appear very nonlinear. However, tension tests to measure this response do not recreate an effective physiological environment. For example, an elongated strip of corneal material first creates an unmeasurable tension, but instead releases water. As a result, tension often rises exponentially beyond the limit in the superphysiological state. However, these complex nonlinearities result in neglecting the mechanism that is linear in most cases, but they have complex pairs. However, as linear complexes of linear mechanisms maintain their linearity, they have some essentially nonlinearity. Ideally, this linearity is fundamentally simple and is magnified by the complexity of most of the overall linear mechanisms. If this is the case, a correctly predicted and widely applicable model will only be spherical after all elements of all superstructure mechanisms have been fully integrated.

Superstructurally, the cornea is oriented fibers (lamella) arranged primarily in layers and glycosaminoglycans (GAGs), partially bound and partially spaced by a hydrophilic matrix filled with free water, as shown schematically in FIG. A complex composite material consisting of 10002. Therefore, accurate modeling tools should include or explain the following facts.

1. The member under stress is not a shell but a layer of fibrils. Intraocular pressure (IOP) keeps fibrils under tension. This tension is distributed evenly over the thickness of the cornea (for example, the front and back fibers are usually equally stressed).

2. The superimposed fibrous layers are crossed (near the circumference). The cornea of the human body has a dominant specific orientation of fibrils. Other geometric factors, such as directionality and rate of periphery of corneal thickness measurements, vary by race. Thickness non-uniformity (eg thin spots in the nose) is elevated from the non-uniformity of the fibrous layer and actually develops.

3. A relatively large circumferential stress at the juncture junction (where the 8 mm radius surface joins the 12 mm radius surface) is supported by the circumferential fiber ring 10004.

4. Scleral fibers are not organized into large parallel fibrillar layers but intersect. The minimum scleral thickness occurs at its equator (with respect to the optical or eye axis of symmetry).

5. The surface shape is determined by the fibrous length and stabilized by the layer interconnects. Standard (eg, healthy and preoperative) shapes are not affected by large changes in intraocular pressure. Under these moderate stresses, the fibrils do not extend markedly.

6. When the fibrils are cut, a change in surface shape occurs, the stress is unevenly redistributed and the unloaded fibrous layer expands. This expansion is determined by the complex interaction of the fibrillar and the crosslinking stress with the internal fibrillar matrix pressure. See Robert's book.

7. Fibrillar spacing for transparency is precisely maintained. This requires an interstitial structure observed as a multilayer of tiled fibers (fibers that are a compact group of parallel fibrils).

8. Increased opacity around the outer periphery of the cornea, especially around the lamellar layer, indicates a decrease in fibrous tissue around the lamellar layer (eg, an increase in fibrillar crossover).

9. Fibrillar spacing is maintained by the complex balance between the spring-spaced material (internal fibrillar GAGs) and the fluid pressure (which is always about -60 mmHg as relatively negative). Negative pressure or suction (absorption) is maintained by the endothelium.

10. Beyond the physiological range, corneal thickness hydrates proportionally. Resected epilepsy in salt expands to 150% of its physiological value per hour. When pressed in salt, a significant amount of inflation pressure can be measured. When inhalation is applied in response to inflation pressure, the negative pressure absorption pressure can be measured.

11. Inflation and absorption The matrix pressure is greater than the intraocular pressure to generate tension in the fiber. Thus, the fibrillar matrix pressure can never be ignored.

12. Internal fibril crosslinking, matrix composition, fibrillar layer structure and fibrillar orientation are all spatially dependent within the cornea. Local fiber layer orientation is responsible for the observed non-uniform meridian elongation caused at least in part by excess intraocular pressure.

13. The cornea relaxes when it is young and becomes more rigid as you age. This is probably due to increased crosslinking and / or stiffness of the internal fibrillar matrix (via accumulation of various molecular species) with age.

Corneal fiber model

To illustrate the invention, the fibers are theoretically defined as a compact group of fibrils. Thus, fibers are modeling structures rather than physiological entities. The corneal fiber model assumes three things:

1. The fiber is followed by the shortest line. Corneal fibers are not resistant to bending moments and, therefore, are mostly placed under pure tension. Fibers under pure tension are followed by a straight line, which is the shortest line of the surface (eg, a large circle of spheres) when limited to a given surface.

2. The fiber is tiled on the surface. All layers are individual tiles of fibers. The gap created by the intersection produces significant optical diffusion and is thus avoided.

3. The fiber area is preserved. The number of fibrils and fibrillar spacing are preserved. Thus, as the fiber is defined by separate genes containing it, the fiber region must be conserved.

The technique described below with reference to Figure 15 will assist the reader in understanding the corneal fiber model according to the present invention. The limbal plane 10020 is a plane that is optimally fitted to the limbal part. Corneal apex 10022 is the central anterior surface point furthest from the limbic plane. The corneal axis 10004 is perpendicular to the limbal plane that intersects the apex of the cornea. The meridian surface 10006 _n includes the corneal axis. The central fiber of any layer is one that intersects the corneal axis. The layer fiber furthest from the center fiber is the lateral fiber. In any layer, the median plane is the meridian plane that intersects the central fiber at right angles.

The following sequence can be deduced immediately from model assumptions.

1. The fiber aspect ratio gradually changes from the center to the circumferential position. If the fiber area is preserved and the fiber is always the shortest line, the fiber lying on the convex surface should be thinnest in the center and increase outward. This partly explains why the epilepsy thickness increases toward the outer circumference. However, we assume that the observed increase in thickness is not completely explained by the individual fiber aspect ratios. In order to regenerate the standard human body thickness distribution, different fibers of the same layer must have different areas and the areas increase from the center toward the lateral fibers.

2. The lateral fibers blend naturally into the columnar fiber ring. The shortest line orientation causes the lateral fibers to blend outward. Thus, the lateral fibers readily flow into the leap fiber ring.

3. Corneal orientation (tilting) causes scleral dissolution. The fibers tilted into a single layer follow the shortest line so that the release crosses all fibrils at two opposing opposing points. Also by using this spherical example, the diversity of overlapping layers that intersect together at all angles may include the trajectory of the opposing points that intersect the fibrils that all intersect at the equator. Topologically, this means of tilting evenly across the cornea leads to broad fibrils intersecting in the annular annular zone.

How is the shape of the cornea determined? If the fiber is formed under tension, a flat surface can be predicted. However, it has long been observed that the development of the cornea must be pressed to properly form. The final shape can be determined by the initial arrangement of ectoderm cells to generate interstitial fibrils. The pressure bulges this cell layer into a dome shape. As the fibers lay down, they follow the cell layer. Eventually the fibrous layer is sufficiently thick and sealed (by connecting the GAGs) so that the layer can resist pressure on itself. This allows the fibers to be placed under tension to form a surface having a shape maintained by a pre-fixed fibril length. The repeated layer is added to the surface with the fibril following the surface shortest line.

The fibers do not follow the shortest line outside the cornea. For example, the pontoon ring fibers do not follow the shortest line. In addition, it does not thicken the sclera at the rear pole, which is the result of the minimum equator thickness when following the shortest line. What is the difference between cornea and sclera layup? The parallel state of the corneal fibers does not allow lateral fiber flexural forces. Thus, corneal fibers are followed along the shortest line. The interwoven sclera fibers exert lateral forces on each other and follow a non-short curve.

Corneal fibrils are preserved. This can be deduced from repeated observations where the fibrils are not visible at the ends but appear to span the cornea from the limbus to the limbus (and beyond). Fibrillar ends are few or terminated at a given confluence with other fibers and are very difficult to detect. Fibrillar preservation can be calibrated precisely because it is difficult to plan whether any unfinished fibrils will be constructed.

Hydrated Matrix Model Matrix Model

Corneal fibers are curved by the degree of pressure gradient set by intraocular pressure. For example, if the layer surface is spherical, the pressure gradient is

Is perpendicular to the given surface. P is intraocular pressure, Is the film stress and R is the film radius. It is known that the fibers are almost equally stressed and the layer radius is nearly uniform throughout the cornea depth. Thus, the pressure gradient is nearly constant throughout the cornea. However, the mechanically induced pressure gradient is only part of the figure. Hydraulic pressure in the cornea (actually suction) must control the internal fibrillar spacing. Precise prediction of corneal morphology should include two mechanisms: fibrillar curvature with pressure gradient and internal fibrillar spacing with hydration balance.

The glycosaminoglycan matrix, which maintains the internal fibrillar spacing, is very hydrophilic. Absorbed water expands the matrix and fibrillar spacing is controlled by controlling corneal hydration. Physiologically steady state is a non-hydrated state that requires negative internal pressure for homeostasis. Thus, the mechanical image of the matrix is one of the elastic materials in a pressurized state by relatively negative hydraulic pressure. The "elastic constant" of the matrix can be inferred by measuring inhalation or expansion pressure. "Suction" is the hydraulic pressure of the negative pressure in the matrix. "Expansion" is the positive reaction pressure of the pressurized matrix. The form of measurement of positive expansion pressure can be expressed as

Although mechanically expressed, it should be remembered that the matrix elastic force is driven by suction, ie by bonding water molecules to hydrophilic GAGs. Therefore, depending on the temperature and as the temperature increases Decreases. Hydration (H) is defined as the water mass divided by the dry mass of the cornea (both fibrils and matrix). Associated expansion pressures are useful on H phases in the range 1-10. The thickness T of the cornea is linearly related to the hydration (dT / dH) equivalent to 0.14 mm / H for the human cornea.

Dry mass density of cornea ) Is virtually identical for virtually all mammals.

Two hydration matrix equations coupled to these composite fiber mechanics (H) and T (H) are sufficient to construct a static model of the cornea.

The outline of FIG. 16 shows normal static hydraulic pressure from outside of the human eye to the inside. Starting with atmospheric pressure 10030 of air, there is a negative jump of about 60 Torr to suction pressure 10032. This rapid drop is pressed across the capsular tissue. In corneal epilepsy 10034, the pressure gradually increases as a whole as IOP. Across the endothelium 10036, there is a negative jump from the front chamber 10038 to a uniform IOP. However, such homeostasis pictures can be altered by surgery and other interventions. As an example, the study [Odenthal, 1999] tested the effects of a two-hour hypoxic stress, known as an overshot, on corneal thickness by an experimental alleviation of buffered vibration. Thus, as long as there is a required dissipative and capacitive element, it cannot be represented by a static equation. Thus, the missing pieces should account for the diffuse migration of water in the cornea. A series of diffusion models must be associated with the existing hydration equations to obtain the transport equations H (x, y, z, t) for H ₂ O in the cornea. Examples of diffusion models include simple diffusion and chemotactic diffusion (chemical diffusion).

In order to make accurate biomechanical predictions, corneas must be measured in general and individually modeled. Thus, a suitable finite element model (FEM) can be used for (a) fibrillar orientation, b) thin membrane size and structure, c) mechanical properties of the thin membrane, d) hydration transport kinetics, e) interstitial structure, f) epithelium, g) Hydrophilic GAG's structure, h) cross-linking between thin layers, and i) can be assured by the inventors that it is an essential component obtained from CUSM consisting of fibrous structure at the edge (circumferential ring). . Each data that is considered necessary to construct an accurate finite element consists of a) local anatomical depth data, b) wavefront data, and c) IOP data. Once the exact values of the Young's modulus and Poisson's ratio have been determined, accurate finite elements can be constructed. Preferably, the finite element is a three-dimensional, anisotropic, layered solid element with 20 nodes. Once the finite element is constructed, the invasion process can be simulated and this modeling result can be compared with real data from real surgical results. The finite element can then be deformed repeatedly until the simulation process matches the measured response. The output of the optimal model then provides the best prediction indication for the proposed surgical eye correction.

Corneal finite element model

In accordance with an embodiment of the present invention, the corneal simulation model 500 shown in FIG. 17 includes a sclera 502, a leap 504 and a cornea 506, wherein the cornea front / rear view in the optical region is (US It is determined from a diagnostic test obtained with the Orbscan corneal analysis system of Roche, Rochester, New York. The sclera, circumference and periphery of the cornea are presumed to form an elliptical shape that shifts to the corneal surface measured at the edge of the optical region. 18 shows an ablation finite element mesh 508 of the corneal model.

As shown in FIG. 19, an upright stratified brick element 510 is used to represent all areas of the eye, and the material properties and material orientations for each layer 512 _n define the overall properties of each area. Function In such sclera, the layer properties are uniform and produce anisotropic responses in the transverse direction, with thin plates of the circumference predominantly having peripheral edge orientation and high hoop stiffness. The lamina of the cornea has an arbitrary orientation near the posterior surface and more predominantly changes in the vertical orientation near the anterior surface. This orientation is shown by five layers of elements 512 ₁ through 512 ₅ , indicated by element number 550 in FIG. 20.

Material properties for each finite element layer (up to 100 layers per element with 5 to 10 elements through corneal thickness) are: a) epithelium, b) Bowman's layer, c) lamellar layer, d) ground material, e A) a membrane of decemet, or f) an endothelial having the above orientation and structure. The truncated normal distribution is used to sample the layer thickness and plate width and orientation, and the bilinear weighting function is used to change the plate orientation as a function of depth below the posterior surface. In the region where the simulated plate layer first coincides with the defined plate layer, it is assumed that the portion of the layered element consists of ground material. It is also presumed that the plate layer extends from the leap to the leap along the meridian with varying thicknesses consistent with a constant cross sectional area. The parameters of the sampling distribution can be chosen to represent a wide range of estimates related to the sheet geometry and the layered plate interactions.

The basic structural load on the cornea is IOP, which dilates the eyeballs. Thus, the element formula is related to the stress stiffness effect to explain the breakdown pressure. Nonlinear geometric effects are included in the evaluation of the finite element response. Incision between finite elements can also be simulated by releasing the connection between elements adjacent to the incision plane. This is accomplished by defining a pair of nodes along a positional incision and mathematically tying them together. The actual incision is then simulated by successively releasing the bundle. An example of releasing the elements is shown in FIG.

According to a preferred embodiment of the present invention, the finite element analysis approach involves all of the structural specificity of the human cornea and the measured behavior associated with additional data on the structure of the human eye. By combining the information with specific information from the patient, the structural measurements are then incorporated into a 3D model of the patient's eye. The problem is then solved by the equation F = Ma + Cv + kx, where M is the mass of the object, a is the acceleration of the object, C is the damping constant for internal vibration, v is the velocity, k is The stiffness matrix for the elastic deformation of the material, x is the magnitude of the displacement. The equation contains all the information needed to predict the mechanical behavior of the human cornea. The equation may be nonlinear if it becomes more mathematically complex. The actual solution to this equation may require a solution of a system of nonlinear partial differential equations (PDE's). The differential equation can be solved by finding a solution to weaken the shape of the PDE's. However, it can be seen that the math required to solve this corneal problem is the same as the math to solve any material deformation problem. Embodiments of the present invention are then structural relationships that depend on the interior of the element and the structural specificity that occurs between the elements. Given the structural features of these elements, solutions can be found for corneal response systems. This embodiment is configured to recalculate corneal structural properties according to the grade of the patient and to provide predictive analysis of corneal structural responses by any action applied to the cornea. An example of such structural characteristics is shown in the flow diagram 600 shown in FIG. In step 602, the scleral elliptical parameter is specified. Such a parameter may be obtained from the axial length measurement of the eye or a general value obtained from the normal population may be used. In step 604, the patient corneal geometry is determined. Preferably, it is a front chamber geometry and more preferably can be obtained from a non-uniform rational water based spline (NURBS) obtained by an Obscan pretreatment experiment. In other embodiments, appropriate data can be obtained by OCT or C-scan (ultrasound) measurements. In step 606, the 3D solid geometry of the entire eyeball with cornea, limbus and sclera may be formulated (as shown in FIGS. 17 and 18). In step 608, the incision / ablation surface is identified based on the best estimate of the predictive surgical plan. The platen of the cornea is simulated with the platen plate 514 of FIG. 17, wherein the 1 mm deformed cornea is cut and enlarged in FIGS. 23 and 24, respectively. In step 610, a default finite element size is selected and a finite element mesh is generated as shown in FIG. A spherical element coordinate system is used and the edges of the elements coincide with the incision / ablation plane. Essentially, the element is incised such that the elements engage and disengage in place. In step 612, the element layer is defined. The process for each layer is as follows.

a) specify substances such as epithelial, Baumann's layers, lamina, ground materials, dimethetic membranes or endothelium,

b) specify the layer thickness,

c) The position of the thin plate and the orientation of the layer are specified by the following process.

i) select the starting point or perimeter (0 to 360 degrees),

ii) select plate orientation (-90 to 90 degrees, depth function),

iii) select the sheet width (1-4 mm),

iv) extrude each plate from edge to edge,

v) if it is not obstructed or partially blocked by other plate layers in the layer, reduce the width and complete projection, otherwise define it as ground material,

vi) Have you had the maximum number of processed layers?

If not return to c),

If yes, define all unspecified layers as ground material and proceed to the next layer,

vii) After defining the ground material and plate properties for all layers, apply individual elements based on the position of the element centers.

In step 612, a boundary condition is defined. Preferably, displacement suppression and individual IOP values in the sclera are included. In step 616, the basic material parameters of the system are specified. This includes zero coefficients (E _x, E _y, E _z ), Poisson's ratios (V _xy , V _yz , V _xz ) and shear coefficients (G _xy , G _yz , G _xz ). In step 618, the incision / ablation surface is released and an incremental nonlinear solution is performed. Finally, in step 620, the model corneal shape is compared with the measured post-treatment data. If you agree with the shape, the finite element is accurately modeled. If the shape is not satisfactory, the method returns to step 616 where the material parameter is deformed, and steps 618 and 620 are repeated. The final result of the modeling is an accurate finite element model of the patient's "grade" that can be used as predictive information when new patients belonging to a particular patient grade are evaluated for surgery in accordance with the present invention.

Figure 2 shows the overall system configuration 200 for the Lasik procedure incorporated with the present invention as described in the above embodiment. The diagnostic station 210 is preferably associated with a deviation for wavefront measurements, and may include other devices or autorefractors for objective or subjective refraction data, tonometers for IOPs, and others known in the art, A suitable diagnostic instrument 212 may be included, such as a topographical device for measuring corneal geometry. Diagnostic output 215 representing new information about the patient is provided with graphical user interfaces (GUIs) for surgical and custom lens applications and others (not shown), optimized real and theoretical historical results databases, and capture / analysis software. It is sent to a computer 220 that includes a structural and functional configuration 222. The analysis of diagnostic information 215 associated with the historical information is provided in the form of a best prediction result indication 217 incorporated at 219 along with the planning software 230 procedure. The non-limiting list of non-consumable or corrective procedures 232 includes myopia, astigmatism, hyperopia, primitive astigmatism, recalibration (eg, prior to defocusing), mixed astigmatism, PRK, Lasek, and the like. The information is then incorporated with physical removal profile software 240, which may take into consideration factors 242 such as optical area size, transition area size, custom contact lens design, etc., at 239. The information is incorporated with other clinical and biofunctional modifications 250, which may be partly or wholly accessible to the Internet, as shown, for example, at 252. The information is modified at 259 by personalized surgical nomogram 260. All this analyzed information is used at 269 to generate the theoretical surgical plan 270 sent to the laser driver software 280 to drive the therapeutic laser 290. Such a system is implemented in, for example, the Zyoptix® system from Bash & Rom Incorporated, which is incorporated with a Zylink® 2.40 version algorithm package. As shown, the optimized theoretical surgical plan 270 and the actual historical result data 292 are used to continuously update the data structure 220 to provide the best prediction result indication for the calibration process.

Another embodiment of the present invention showing a system 300 for providing predictive results for eye treatment correction such as photoablative corneal remodeling is shown by the block diagram of FIG. 3. Diagnostic station 302 is provided to obtain new measurements of the eye condition of the patient's eye 320. Diagnostic station 302 may output a new information metric 305 learned by a particular diagnostic capability. The data collection and transmission station 308 is suitably connected to a diagnostic station 302 for accepting a new diagnostic input 305 at 304. The data collection and transmission station 308 is also configured to selectively accept new predictive treatment outcome impact information 306 that is different than that provided by the diagnostic station 302 as shown by arrow 307. Such information includes patient profile data, practitioner data, ambient data, and the like, which may be entered manually at station 308 manually via a keypad or CD or automatically input by a suitable sensor that has recorded good information. The data collection and transmission station 308 is connected to a computing station 310 similar in form and function to the computing station 11 described above in connection with FIG. The treatment station 318, which preferably comprises a flying spot, an excimer laser system and an eyetracker, is in communication with or otherwise connected to the data collection and transmission station 308 to receive the output 314. Is communicatively coupled to the computing station 310 to receive 322. Regardless of which station 308, 310 is the source of the final output 316, the output is in the form of a custom photodissection algorithm for driving the therapeutic laser system to facilitate correction of visual defects in the patient. Best prediction indication. Various stations may be properly located or remotely located to collect information and perform procedures performed by the present invention. As described, the best prediction indication, the final result of the present invention described herein, can be used to drive custom contact lenses, IOL, inlay or onlay configurations.

In another embodiment of the invention, the invention relates to executable instructions embodied as a delivery means to a user to provide predictive results for the therapeutic eye correction or eye vision described above. The instructions are delivered as surgical parameters such as, for example, LASIK, corneal ablation depth or optical area size recommendations for photoresection surgery, and are performed by a practitioner or with a custom contact lens or IOL. In a related embodiment, the instructions are, but are not limited to, delivered via a means or computer or device-readable medium, such as, for example, a dark disk, CD, land or satellite-based data stream, and the ablation for a therapeutic laser system. It is performed according to an instruction such as an algorithm or ablation shot profile.

In another embodiment shown in FIG. 10, the present invention provides a selection apparatus 1004 for facilitating selection of collected information for analysis into a data structure of optimal historical information that is an indication of a result prediction influencing the visual calibration process; An eye diagnosis and / or treatment system 1000 that includes a diagnostic 1003 and / or treatment 1005 component having a graphical user interface 1001 with a display 1002. In the system 1000 according to the present invention, the method of providing and selecting from the menu 1007 on the display 107 comprises the following steps: a) a menu in which each menu entry represents a predictive, eye, treatment outcome impact characteristic. Retrieving a set of menu descriptions from 107, b) receiving a menu selection signal of the selection device pointing at a menu item selected from the set of descriptions of the menu, and c) in response to the signal, an optimized actual And combining an analysis of the description of the selected menu in relation to the data structure of the theoretical historical information, which analysis generates the best prediction indications relating to the results for eye treatment correction and lens design.

4 illustrates, in a flow chart manner, processes 400 performed by the systems 100, 200, 300, and 1000 shown in FIGS. 1, 2, 3, and 10, respectively. At block 410, a plurality of predicted and known new treatment outcome impact information is collected from various sources 401, 402, 403. This new information includes the patient's eye defect information and various other information related to the patient, practitioner, diagnostic and therapeutic instrument. At block 410, a plurality of predictive and known new treatment outcome impact information is collected from various sources 401, 402, 403. This new information includes the patient's eye defect information and various other information related to the patient, practitioner, diagnostic and therapeutic instrument, and the local environment. At block 420, the optimized (statistically) history, treatment outcome information is stored along with theoretical surgical plan information 405. New information related to the correction of visual defects in the patient is analyzed in relation to the history and optimized treatment outcome information. At block 430, the best prediction indication 416 is generated and forwarded to the treatment device / operator 403. Advantageously, said best prediction indication is an optimized custom photoresection algorithm performed (but not necessarily limited) to drive the laser system and provide good patient vision correction. The indication may be optimized by statistical analysis, multi-variable matrix operations, neural network processing and / or other methods known in the art.

In one aspect of an embodiment of the method, the best prediction indication is provided to the practitioner by a third party on a fee basis or contract basis as shown at 440. Typically, each physician around the world is limited to historical outcome based ownership of their own practice. As can be argued, it is beneficial to have a large amount of execution sufficient and to allow the physician access to a large database of optimized historical results information as a source for providing visual correction treatment. By way of example, such a database may give useful information to the practitioner (and others) about fees or other considerations. Historical database entries may be obtained from a proprietary database from another person for fees or other fees. This is beneficial for expanding and updating the historical results database. The third-party database owner may have optimized outcome prediction instructions on a beneficial basis in response to the practitioner's request for such instructions based on the relative outcome impact information provided to the third owner by the practitioner and the patient's eye defects. As an example, an ablation algorithm for driving a photo ablation laser system may be provided to the practitioner. Data provided by the practitioner may be manually acquired and / or automatically transmitted by a third party analyzing information associated with a large results database (preferably thousands of cases). The third owner then sends an optimal result prediction indication to the practitioner who should provide the patient with the best visual outcome. Depending on the practitioner's instrument, the practitioner allows the patient to simulate predictive treatment so that the patient may know about the postoperative visual acuity prior to surgery, such as by other methods including eye surgery. This simulation is performed by a phoropter device 1113 or by a printer 1111 or GUI 1001 with a deformable mirror or other phase compensation means known in the art, as shown in FIG. The various characters provided may be represented graphically or in other visual forms.

While various advantageous embodiments have been selectively shown in the present invention, those skilled in the art will recognize that various changes are possible within the spirit of the invention as defined by the appended claims.

Claims (63)

The system provides predictive results for the proposed therapeutic eye correction, Collection for collecting new information metrics, practitioners, diagnostic measurements, treatment status and ambient conditions related to at least one patient that are predictive and affect treatment outcomes, and for transmitting a plurality of new information to the computing station And a transmission station, The computing station a) receive new information metrics, b) store predictive and influential treatment results, and a plurality of optimized historical treatment results information derived from practitioners, diagnostic measurements, treatment conditions and surrounding conditions associated with at least one patient, c) means for providing an output comprising the best prediction indication derived by analyzing new information related to the optimized historical information to facilitate improved therapeutic eye correction. The system of claim 1, wherein the best predictive indication is an algorithm that describes a laser ablation shot placement pattern in a patient's eye. The system of claim 1, wherein the best prediction indication comprises an outcome prediction, eye information metric suitable for use by a practitioner to provide therapeutic eye correction. The system of claim 1, wherein the optimal analysis is a statistical analysis. The method of claim 1, wherein the optimal analysis comprises a vector Z representing new diagnostic information, a clinical matrix M 'representing a plurality of Zernik terms or their equivalents, and another matrix M representing historical result information. ") And a matrix analysis comprising a result matrix (Z ') representing the best prediction indication. 6. The system of claim 5 comprising a feedback loop provided by an update information metric supplied to said matrix (M "). The system of claim 5 wherein the component of M ″ represents information from a plurality of sources. 6. The system of claim 5 wherein the component of Z is the Zernnik vector or its equivalent output from the wavefront detection device. 5. The system of claim 4, wherein the computing station comprises a data structure employing a neural network for generating the best prediction indication. The system of claim 1, wherein the best prediction indication is a postoperative spherical aberration value (Z _{400 Post} ) for a given pupil size. The system of claim 10, wherein the postoperative spherical aberration value (Z _400Post ) is a preoperative spherical aberration value (Z _400Pre ), preoperative out of focus value (Z _200Pre ), and a constant factor (± C). 12. The system of claim 11, wherein Z _{400 Post} = A * Z _{400 Pre} + B * Z _{200 Pre} + C, where A and B are (±) constants for a given pupil size. The system of claim 1, wherein the computing station is physically in close proximity to the collecting and transmitting station. The system of claim 1, wherein the computing system is located away from the collecting and transmitting station. A system that provides predictive results for therapeutic eye correction, A computing station having a data structure with an optimal historical treatment result information metric derived from an optimal analysis of a plurality of historical information metrics affecting the treatment result, The computing station is configured to receive new treatment outcome impact information of the plurality of predictions including at least eye defects related to the patient, wherein the computing station is configured to receive the best prediction outcome indication based on the analysis of the new information related to the historical outcome information. A system configured to be properly formed and provided. The system of claim 15, wherein the data structure is a neural network. The system of claim 15, wherein the best prediction result indication is an algorithm that describes a laser ablation shot placement pattern of a patient's eye. The system of claim 15, wherein the best prediction result indication is a postoperative spherical aberration value (Z _{400 Post} ) for a given pupil size. 19. The system of claim 18, wherein the postoperative spherical aberration value (Z _{400 Post} ) is _dependent solely on the preoperative spherical aberration value (Z _{400 Pre} ), preoperative focus out of focus value (Z _{200 Pre} ) and constant factor (± C). 20. The system of claim 19, wherein Z _{400 Post} = A * Z _{400 Pre} + B * Z _{200 Pre} + C, where A and B are (±) constants for a given pupil size. The system of claim 15, wherein the optimal analysis is a statistical analysis. 22. The method of claim 21, wherein the optimal analysis includes a vector Z representing new diagnostic information, a clinical matrix M 'representing a plurality of Zernik terms or their equivalents, and another matrix M representing historical result information. ") And a matrix analysis comprising a result matrix (Z ') representing the best prediction indication. 23. The system of claim 22 comprising a pitback loop provided by an update information metric supplied to said matrix (M "). 23. The system of claim 22 wherein the component of M ″ represents information from a plurality of sources. 23. The system of claim 22 wherein the component of Z is the Zernnik vector or its equivalent output from the wavefront detection device. A system that provides predictive results for therapeutic eye correction, a) a diagnostic station capable of learning new eye information metrics from a patient and having the ability to output new information metrics; b) a data collection and transmission station cooperatively coupled to said diagnostic station, said data collection and transmission station having the ability to accept and output said new information metric; c) a data structure in communication with said data collection and transmission station, comprising a data structure with optimal history treatment result information, collecting and transmitting information and analyzing each new information metric associated with said optimal history treatment result information. A computational station having the ability to generate the best prediction instruction, d) a treatment station cooperatively coupled to said computing station, said treatment station having the ability to execute the best prediction instructions. 27. The system of claim 26, wherein the data structure comprises a neural network. 27. The system of claim 26, wherein the best outcome prediction indication is an algorithm describing a laser ablation shot placement pattern of a patient's eye. 27. The system of claim 26, wherein the best outcome prediction indication is postoperative spherical aberration value (Z _{400 post} ) for a given pupil size. 30. The system of claim 29, wherein the postoperative spherical aberration value (Z _400post ) is _dependent solely on the preoperative spherical aberration value (Z _400pre ), preoperative focus out of focus value (Z _200pre ) and constant factor (± C). _31. The system of claim 30, wherein Z _{400 Post} = A * Z _{400 Pre} + B * Z _{200 Pre} + C, where A and B are (±) constants for a given pupil size. 27. The system of claim 26, wherein the optimal analysis is a statistical analysis. 33. The method of claim 32, wherein the optimal analysis comprises a vector Z representing new diagnostic information, a clinical matrix M 'representing a plurality of Zernik terms or their equivalents, and another matrix M representing historical result information. ") And a matrix analysis comprising a result matrix (Z ') representing the best prediction indication. 34. The system of claim 33, further comprising a feedback loop provided by an update metric provided in said matrix (M "). 34. The system of claim 33 wherein the component of M " represents information from a plurality of sources. 34. The system of claim 33, wherein the component of Z is a Zernnik vector or its equivalent output from the wavefront detection device. To provide predictive results for therapeutic eye correction, a) collecting a new treatment outcome impact information metric comprising at least eye defect information about the patient, b) analyzing new information related to the plurality of optimized historical treatment result information for confirmed eye defects; c) generating with the computing device the best prediction indications to facilitate optimized results of eye treatment fixation. 38. The method of claim 37, further comprising using the generated best prediction indication to drive the treatment system to provide eye correction. 38. The method of claim 37, wherein collecting includes automatically collecting new information metrics. 38. The method of claim 37, wherein said generating step comprises a statistical analysis. 38. The method according to claim 37, wherein said generating step comprises a clinical matrix representing an interdependence relationship between a result matrix Z 'representing a best prediction indication from a vector Z' representing new diagnostic information and a plurality of Zernik terms or their equivalents. A matrix analysis comprising a matrix (M ') and another matrix (M ") representing historical result information. To provide predictive results for therapeutic eye correction, a) acquiring new information metrics related to the patient's eye defect status, b) maintaining a database of optimized historical eye result information related to the eye defect condition; c) providing the best predictive indication for the therapeutic eye correction provided in the interaction base. 43. The method of claim 42, wherein acquiring the new information metric comprises collecting wavefront aberration data from the wavefront detection device. 43. The method of claim 42, wherein maintaining the database of best historical eye result information includes updating a database with useful eye correction result information and optimizing the historical result information. 43. The method of claim 42, wherein optimizing the historical result information comprises statistical analysis of the historical result information. 46. The method of claim 45, wherein optimizing the historical result information comprises coupling the neural network to the historical result information and the useful historical result information. 43. The method of claim 42, wherein maintaining a database of optimized historical eye result information includes acquiring new historical result information from a third party for a fee. 43. The method of claim 42, wherein providing the best prediction indication on the interaction base comprises accepting a fee or other remuneration. A computer-readable or device-readable medium having stored thereon performance instructions that provide predictive results for therapeutic eye correction, wherein the instructions are associated with an optimized historical treatment outcome information metric and associated with a new information metric related to the patient's eye condition. Medium that is the best prediction indication derived from the analysis. The medium of claim 49 wherein the executable indication is an algorithm that describes a laser ablation shot placement pattern of a patient's eye. 50. The medium of claim 49 wherein the best prediction indication comprises an outcome prediction, eye information metric suitable for use by a practitioner to provide therapeutic eye correction. A data structure associated with a computing device that generates the best predictive indication for therapeutic eye correction, The data structure execution method, a) accepting new information metrics related to the patient's eye defect status, b) maintaining a database of optimized historical eye result information related to eye defect conditions; c) generating a best prediction indication for the therapeutic eye correction. 53. The data structure of claim 52, wherein said best prediction indication is provided on an interaction base. 54. The data structure of claim 53, wherein providing the best prediction indication on the interaction base comprises accepting a fee or other remuneration. 53. The data structure of claim 52, wherein acquiring the new information metric comprises collecting wavefront aberration data from a wavefront detection device. 53. The data structure of claim 52, wherein maintaining the database of optimized historical eye result information includes updating the database with useful eye correction result information and optimizing historical result information. 59. The data structure of claim 56, wherein optimizing the historical result information includes a statistical analysis of the historical result information. 59. The data structure of claim 56, wherein optimizing the historical result information comprises coupling to neural networks to analyze the historical result information and the useful historical result information. 53. The data structure of claim 52, wherein maintaining the database of optimized historical eye result information includes acquiring new historical result information from a third party for a fee. The system of claim 1, wherein the optimal analysis is finite element analysis using a finite element model (FEM), wherein the FEM is a layered element with three waves, anisotropy, nonlinearity, viscosity and elasticity. The system of claim 15, wherein the optimal analysis is finite element analysis (FEA) using a finite element model (FEM), wherein the FEM is a layered element having three dimensions, anisotropy, nonlinear viscosity and elasticity. 27. The system of claim 26, wherein the optimal analysis is finite element analysis (FEA) using a finite element model (FEM), wherein the FEM is a layered element having three dimensions, anisotropy, nonlinearity, viscosity and elasticity. 38. The method of claim 37, wherein the generating step comprises performing a finite element analysis (FEA) using a finite element model (FEM), wherein the FEM is a layered element having three dimensions, anisotropy, nonlinear viscosity and elasticity. How to be.